SoroushVahidi/scidocs huggingface dataset:
The COPS Security Checker System
(1990)

In the past several years, there have been a large number of published works that have graphically described a wide variety of security problems particular to UNIX. Without fail, the same problems have been discussed over and over again, describing the problems with sum (set user ID) programs, improper file permissions, and bad passwords (to name a few). There are two common characteristics to each of these problems: first, they are usually simple to correct, if found; second, they are fairly easy to detect. Since almost all systems have fairly equivalent problems, it seems appropriate to create a tool to detect potential security problems as an aid to system administrators. This paper describes one such tool: Cops. (Computerized Oracle and Password System) is a freely-available,
NetSTAT: A Network-Based Intrusion Detection Approach
(1998)

Network-based attacks have become common and sophisticated. For this reason, intrusion detection systems are now shifting their focus from the hosts and their operating systems to the network itself. Network-based intrusion detection is challenging because network auditing produces large amounts of data, and different events related to a single intrusion may be visible in different places on the network. This paper presents NetSTAT, a new approach to network intrusion detection. By using a formal model of both the network and the attacks, NetSTAT is able to determine which network events have to be monitored and where they can be monitored.
The Impact of Indoor Lighting on Students ’ Learning Performance in Learning Environments : A knowledge internalization perspective
(2013)

The purpose of this study is to identify the influence of indoor lighting on students’ learning performance within learning environments from knowledge internalization perspective. This study is a comprehensive review of literatures base on the influence of indoor lighting on people’s productivity and performance especially students’ learning performance. The result that comes from this study shows that it is essential to improve lighting in learning environments to enhance students’ learning performance and also motivate them to learn more. In this study the researchers utilized Pulay (2010) survey and measured the influence of lighting on students’ learning performance. Utilizing survey data collected from 150 students from Alpha course in Malaysia. This study found significant impact between lighting quality and students’ learning performance this finding is also supported by interview from two experts.
Schema Theory for Semantic Link Network
(2008)

Semantic link network (SLN) is a loosely coupled semantic data model for managing Web resources. Its nodes can be any types of resources. Its edges can be any semantic relations. Potential semantic links can be derived out according to reasoning rules on semantic relations. This paper proposes the schema theory for SLN including the concepts, rule-constraint normal forms and relevant algorithms. The theory provides the basis for normalized management of SLN and its applications. A case study demonstrates the proposed theory.
Design considerations on low voltage synchronous power MOSFETs with monolithically integrated gate voltage pull-down circuitry
(2012)

In this paper, a monolithically integrated gate voltage pull-down circuitry is presented to avoid the unintentional C·dV/dt induced turn-on. The concept of a low threshold voltage MOSFET with this integrated gate voltage pull-down circuitry is introduced as a contributing factor to the next generation high frequency DC-DC converter efficiency improvement. Design considerations on this new device and influences of critical design parameters on device/circuit performance will be fully discussed. In synchronous buck application, this integrated power module achieves more than 2% efficiency improvement over reference solution at high operation frequency (1MHz) under 19V input and 1.3V output condition.
A Framework for Dynamic Image Sampling Based on Supervised Learning
(2016)

Sparse sampling schemes can broadly be classified into two main categories: static sampling where the sampling pattern is predetermined, and dynamic sampling where each new measurement location is selected based on information obtained from previous measurements. Dynamic sampling methods are particularly appropriate for pointwise imaging methods, in which pixels are measured sequentially in arbitrary order. Examples of pointwise imaging schemes include certain implementations of atomic force microscopy, electron back scatter diffraction, and synchrotron X-ray imaging. In these pointwise imaging applications, dynamic sparse sampling methods have the potential to dramatically reduce the number of measurements required to achieve a desired level of fidelity. However, the existing dynamic sampling methods tend to be computationally expensive and are, therefore, too slow for many practical applications. In this paper, we present a framework for dynamic sampling based on machine learning techniques, which we call a supervised learning approach for dynamic sampling (SLADS). In each step of SLADS, the objective is to find the pixel that maximizes the expected reduction in distortion (ERD) given previous measurements. SLADS is fast because we use a simple regression function to compute the ERD, and it is accurate because the regression function is trained using datasets that are representative of the specific application. In addition, we introduce an approximate method to terminate dynamic sampling at a desired level of distortion. We then extend our algorithm to incorporate multiple measurements at each step, which we call groupwise SLADS. Finally, we present results on computationally generated synthetic data and experimentally collected data to demonstrate a dramatic improvement over state-of-the-art static sampling methods
High-Frame-Rate Synthetic Aperture Ultrasound Imaging Using Mismatched Coded Excitation Waveform Engineering: A Feasibility Study
(2016)

Mismatched coded excitation (CE) can be employed to increase the frame rate of synthetic aperture ultrasound imaging. The high autocorrelation and low cross correlation (CC) of transmitted signals enables the identification and separation of signal sources at the receiver. Thus, the method provides B-mode imaging with simultaneous transmission from several elements and capability of spatial decoding of the transmitted signals, which makes the imaging process equivalent to consecutive transmissions. Each transmission generates its own image and the combination of all the images results in an image with a high lateral resolution. In this paper, we introduce two different methods for generating multiple mismatched CEs with an identical frequency bandwidth and code length. Therefore, the proposed families of mismatched CEs are able to generate similar resolutions and signal-to-noise ratios. The application of these methods is demonstrated experimentally. Furthermore, several techniques are suggested that can be used to reduce the CC between the mismatched codes.
Open-Domain Neural Dialogue Systems
(2017)

Until recently, the goal of developing opendomain dialogue systems that not only emulate human conversation but fulfill complex tasks, such as travel planning, seemed elusive. However, we start to observe promising results in the last few years as the large amount of conversation data is available for training and the breakthroughs in deep learning and reinforcement learning are applied to dialogue. In this tutorial, we start with a brief introduction to the history of dialogue research. Then, we describe in detail the deep learning and reinforcement learning technologies that have been developed for two types of dialogue systems. First is a task-oriented dialogue system that can help users accomplish tasks, ranging from meeting scheduling to vacation planning. Second is a social bot that can converse seamlessly and appropriately with humans. In the final part of the tutorial, we review attempts to developing opendomain neural dialogue systems by combining the strengths of task-oriented dialogue systems and social bots. The tutorial material is available at http://opendialogue.miulab.tw.
Rationalizing Neural Predictions
(2016)

Prediction without justification has limited applicability. As a remedy, we learn to extract pieces of input text as justifications – rationales – that are tailored to be short and coherent, yet sufficient for making the same prediction. Our approach combines two modular components, generator and encoder, which are trained to operate well together. The generator specifies a distribution over text fragments as candidate rationales and these are passed through the encoder for prediction. Rationales are never given during training. Instead, the model is regularized by desiderata for rationales. We evaluate the approach on multi-aspect sentiment analysis against manually annotated test cases. Our approach outperforms attention-based baseline by a significant margin. We also successfully illustrate the method on the question retrieval task.1
Discovering Opinion Changes in Online Reviews via Learning Fine-Grained Sentiments
(2016)

Recent years have shown rapid advancement in understanding consumers' behaviors and opinions through collecting and analyzing data from online social media platforms. While abundant research has been undertaken to detect users' opinions, few tools are available for understanding events where user opinions change drastically. In this paper, we propose a novel framework for discovering consumer opinion changing events. To detect subtle opinion changes over time, we first develop a novel fine-grained sentiment classification method by leveraging word embedding and convolutional neural networks. The method learns sentiment-enhanced word embedding, both for words and phrases, to capture their corresponding syntactic, semantic, and sentimental characteristics. We then propose an opinion shift detection algorithm that is based on the Kullback-Leibler divergence of temporal opinion category distributions, and conducted experiments on online reviews from Yelp. The results show that the proposed approach can effectively classify fine-grained sentiments of reviews and can discover key moments that correspond to consumer opinion shifts in response to events that relate to a product or service.
Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty
(2019)

With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban planning and epidemic containment. Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks: people are related in myriad ways, but most graph learning models treat relations as binary. In this paper, we develop a graph-based convolutional network for learning on multi-view networks. We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries. We also show that, while designed specifically for use in poverty research, the algorithm also outperforms existing benchmarks on a broader set of learning tasks on multi-view networks, including node labelling in citation networks.
Coupling CCG and Hybrid Logic Dependency Semantics
(2002)

Categorial grammar has traditionally used the λ-calculus to represent meaning. We present an alternative, dependency-based perspective on linguistic meaning and situate it in the computational setting. This perspective is formalized in terms of hybrid logic and has a rich yet perspicuous propositional ontology that enables a wide variety of semantic phenomena to be represented in a single meaning formalism. Finally, we show how we can couple this formalization to Combinatory Categorial Grammar to produce interpretations compositionally.
Secure data deduplication
(2008)

As the world moves to digital storage for archival purposes, there is an increasing demand for systems that can provide secure data storage in a cost-effective manner. By identifying common chunks of data both within and between files and storing them only once, deduplication can yield cost savings by increasing the utility of a given amount of storage. Unfortunately, deduplication exploits identical content, while encryption attempts to make all content appear random; the same content encrypted with two different keys results in very different ciphertext. Thus, combining the space efficiency of deduplication with the secrecy aspects of encryption is problematic.
 We have developed a solution that provides both data security and space efficiency in single-server storage and distributed storage systems. Encryption keys are generated in a consistent manner from the chunk data; thus, identical chunks will always encrypt to the same ciphertext. Furthermore, the keys cannot be deduced from the encrypted chunk data. Since the information each user needs to access and decrypt the chunks that make up a file is encrypted using a key known only to the user, even a full compromise of the system cannot reveal which chunks are used by which users.
Robust Median Reversion Strategy for Online Portfolio Selection
(2013)

Online portfolio selection has attracted increasing attention from data mining and machine learning communities in recent years. An important theory in financial markets is mean reversion, which plays a critical role in some state-of-the-art portfolio selection strategies. Although existing mean reversion strategies have been shown to achieve good empirical performance on certain datasets, they seldom carefully deal with noise and outliers in the data, leading to suboptimal portfolios, and consequently yielding poor performance in practice. In this paper, we propose to exploit the reversion phenomenon by using robust <inline-formula><tex-math notation="LaTeX"> $L_1$</tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="huang-ieq1-2563433.gif"/></alternatives> </inline-formula>-median estimators, and design a novel online portfolio selection strategy named “Robust Median Reversion” (RMR), which constructs optimal portfolios based on the improved reversion estimator. We examine the performance of the proposed algorithms on various real markets with extensive experiments. Empirical results show that RMR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results. Finally, RMR runs in linear time, and thus is suitable for large-scale real-time algorithmic trading applications.
Activities and well-being in older age: effects of self-concept and educational attainment.
(1998)

The positive effect of activities on well-being is proposed to be mediated by self-conceptualizations and facilitated by socioeconomic status. The hypothesized processes were estimated with LISREL VIII using data from a large cross-sectional survey with a sample of 679 adults aged 65 and older who were representative of older adults living in the Detroit area. Findings indicate that the frequency of performing both leisure and productive activities yields an effect on physical health and depression and that these effects are mediated in part by a sense of self as agentic, but less clearly by a sense of self as social. Furthermore, socioeconomic status, operationalized as formal educational attainment, facilitates the effect of leisure to a greater extent than that of productive activities.
A Hybrid Algorithm for LTL Games
(2008)

In the game theoretic approach to the synthesis of reactive systems, specifications are often given in linear time logic (LTL). Computing a winning strategy to an infinite game whose winning condition is the set of LTL properties is the main step in obtaining an implementation. We present a practical hybrid algorithm—a combination of symbolic and explicit algorithm—for the computation of winning strategies for unrestricted LTL games that we have successfully applied to synthesize reactive systems with up to 10 states.
An IoT-Oriented Data Storage Framework in Cloud Computing Platform
(2014)

The Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of Radio Frequency IDentification (RFID) and wireless sensors devices. Benefiting from RFID and sensor network technology, common physical objects can be connected, and are able to be monitored and managed by a single system. Such a network brings a series of challenges for data storage and processing in a cloud platform. IoT data can be generated quite rapidly, the volume of data can be huge and the types of data can be various. In order to address these potential problems, this paper proposes a data storage framework not only enabling efficient storing of massive IoT data, but also integrating both structured and unstructured data. This data storage framework is able to combine and extend multiple databases and Hadoop to store and manage diverse types of data collected by sensors and RFID readers. In addition, some components are developed to extend the Hadoop to realize a distributed file repository, which is able to process massive unstructured files efficiently. A prototype system based on the proposed framework is also developed to illustrate the framework's effectiveness.
Maintaining Integrity: How Nurses Navigate Boundaries in Pediatric Palliative Care.
(2017)

PURPOSE
To explore how nurses manage personal and professional boundaries in caring for seriously ill children and their families.


DESIGN AND METHODS
Using a constructivist grounded theory approach, a convenience sample of 18 registered nurses from four practice sites was interviewed using a semi-structured interview guide.


RESULTS
Nurses across the sites engaged in a process of maintaining integrity whereby they integrated two competing, yet essential, aspects of their nursing role - behaving professionally and connecting personally. When skillful in both aspects, nurses were satisfied that they provided high-quality, family-centered care to children and families within a clearly defined therapeutic relationship. At times, tension existed between these two aspects and nurses attempted to mitigate the tension. Unsuccessful mitigation attempts led to compromised integrity characterized by specific behavioral and emotional indicators. Successfully mitigating the tension with strategies that prioritized their own needs and healing, nurses eventually restored integrity. Maintaining integrity involved a continuous effort to preserve completeness of both oneself and one's nursing practice.


CONCLUSIONS
Study findings provide a theoretical conceptualization to describe the process nurses use in navigating boundaries and contribute to an understanding for how this specialized area of care impacts health care providers.


PRACTICE IMPLICATIONS
Work environments can better address the challenges of navigating boundaries through offering resources and support for nurses' emotional responses to caring for seriously ill children. Future research can further refine and expand the theoretical conceptualization of maintaining integrity presented in this paper and its potential applicability to other nursing specialties.
Comparing Two IRT Models for Conjunctive Skills
(2008)

A step in ITS often involve multiple skills. Thus a step requiring a conjunction of skills is harder than steps that require requiring each individual skill only. We developed two Item-Response Models – Additive Factor Model (AFM) and Conjunctive Factor Model (CFM) – to model the conjunctive skills in the student data sets. Both models are compared on simulated data sets and a real assessment data set. We showed that CFM was as good as or better than AFM in the mean cross validation errors on the simulated data. In the real data set CFM is not clearly better. However, AFM is essentially performing as a conjunctive model.
Using temporal IDF for efficient novelty detection in text streams
(2014)

Novelty detection in text streams is a challenging task that emerges in quite a few different scenarios, ranging from email thread filtering to RSS news feed recommendation on a smartphone. An efficient novelty detection algorithm can save the user a great deal of time and resources when browsing through relevant yet usually previously-seen content. Most of the recent research on detection of novel documents in text streams has been building upon either geometric distances or distributional similarities, with the former typically performing better but being much slower due to the need of comparing an incoming document with all the previously-seen ones. In this paper, we propose a new approach to novelty detection in text streams. We describe a resource-aware mechanism that is able to handle massive text streams such as the ones present today thanks to the burst of social media and the emergence of the Web as the main source of information. We capitalize on the historical Inverse Document Frequency (IDF) that was known for capturing well term specificity and we show that it can be used successfully at the document level as a measure of document novelty. This enables us to avoid similarity comparisons with previous documents in the text stream, thus scaling better and leading to faster execution times. Moreover, as the collection of documents evolves over time, we use a temporal variant of IDF not only to maintain an efficient representation of what has already been seen but also to decay the document frequencies as the time goes by. We evaluate the performance of the proposed approach on a real-world news articles dataset created for this task. We examine an exhaustive number of variants of the model and compare them to several commonly used baselines that rely on geometric distances. The results show that the proposed method outperforms all of the baselines while managing to operate efficiently in terms of time complexity and memory usage, which are of great importance in a mobile setting scenario.
Applications of SAT Solvers to AES Key Recovery from Decayed Key Schedule Images
(2010)

Cold boot attack is a side channel attack which exploits the data remanence property of random access memory (RAM) to retrieve its contents which remain readable shortly after its power has been removed. Given the nature of the cold boot attack, only a corrupted image of the memory contents will be available to the attacker. In this paper, we investigate the use of an off-the-shelf SAT solver, CryptoMinSat, to improve the key recovery of the AES-128 key schedules from its corresponding decayed memory images. By exploiting the asymmetric decay of the memory images and the redundancy of key material inherent in the AES key schedule, rectifying the faults in the corrupted memory images of the AES-128 key schedule is formulated as a Boolean satisfiability problem which can be solved efficiently for relatively very large decay factors. Our experimental results show that this approach improves upon the previously known results.